Methods for Handling Multiple Outcomes in Health Data
Sivakamakshi Muthu Kumarasamy1, Shivadarshini Sreekanth1
1 School of Mathematical and Statistical Sciences, University of Galway
Background and Problem
Health research often involves multiple outcomes (e.g., survival, disease progression)1.
Current Methods
- Analyse outcomes separately: Treats outcomes as independent and potentially miss biological and temporal links.
- Composite endpoints: May hide heterogeneity in clinical importance, frequency, and treatment response. Losing predictive performance and clinical interpretability.
Emerging Methods
- Clinical trials: Win-ratio preserves the hierarchy of endpoints importance while combining the analysis of the affect2.
- Prediction: Multi-state models can be used to model a process where subjects transition from one state to the next3. And multi-task models are deep learning models which use common pathways to predict multi outcomes of interest4.
Objectives of Project
Compare strategies for both clinical trial design and prediction modelling that treat outcomes independently, as composite endpoints, and via emerging methods.
Data Source
SECOMBIT Trial: Phase II randomised; N ≈ 209 metastatic melanoma; 3 arms5,6.
Endpoints: Overall Survival (OS) and Progression Free Survival (PFS).
Covariates: Sites, Lactate Dehydrogenase (LDH), Tumour Mutational Burden (TMB), a genetic biomarker (JAK).
Baseline Summary Table (by Arm)
| A (N=69) |
B (N=69) |
C (N=68) |
|
|---|---|---|---|
| Sites | |||
| 1 - 2 | 43 (62.3%) | 40 (58.0%) | 42 (61.8%) |
| >= 3 | 26 (37.7%) | 29 (42.0%) | 26 (38.2%) |
| LDH | |||
| Normal | 41 (59.4%) | 39 (56.5%) | 47 (69.1%) |
| Elevated | 26 (37.7%) | 28 (40.6%) | 20 (29.4%) |
| Missing | 2 (2.9%) | 2 (2.9%) | 1 (1.5%) |
| TMB | |||
| < 10 | 20 (29.0%) | 17 (24.6%) | 18 (26.5%) |
| >= 10 | 8 (11.6%) | 8 (11.6%) | 12 (17.6%) |
| Missing | 41 (59.4%) | 44 (63.8%) | 38 (55.9%) |
| JAK | |||
| Wild Type (Normal) | 24 (34.8%) | 17 (24.6%) | 16 (23.5%) |
| Mutated | 5 (7.2%) | 7 (10.1%) | 14 (20.6%) |
| Missing | 40 (58.0%) | 45 (65.2%) | 38 (55.9%) |
Preliminary Analysis
Time-to-event analyses was conducted for Overall Survival (OS) and Progression-Free Survival (PFS) separately. Survival distributions are visualised using Kaplan–Meier plots. Cox proportional hazards models are fitted to estimate covariate-adjusted effects, and as a preliminary predictive modelling strategy.
Kaplan - Meier Plot
Log-rank tests
| Endpoint | Chi.square | df | p.value |
|---|---|---|---|
| OS | 2.80 | 2 | 0.2460 |
| PFS | 7.74 | 2 | 0.0208 |
| Variable | HR (95% CI) | p-value |
|---|---|---|
| ArmB | 0.98 (0.38–2.50) | 0.9590 |
| ArmC | 0.83 (0.33–2.07) | 0.6900 |
| Sites>= 3 | 2.02 (0.93–4.42) | 0.0764 |
| LDHElevated | 0.96 (0.43–2.15) | 0.9130 |
| TMB>= 10 | 0.72 (0.31–1.67) | 0.4480 |
| JAKMutated | 0.63 (0.24–1.63) | 0.3380 |
Cox model — Progression-Free Survival (PFS)
| Variable | HR (95% CI) | p-value |
|---|---|---|
| ArmB | 0.73 (0.30–1.77) | 0.4870 |
| ArmC | 1.03 (0.48–2.22) | 0.9470 |
| Sites>= 3 | 1.60 (0.80–3.20) | 0.1870 |
| LDHElevated | 1.14 (0.56–2.34) | 0.7150 |
| TMB>= 10 | 0.85 (0.40–1.78) | 0.6660 |
| JAKMutated | 0.41 (0.16–1.03) | 0.0571 |
Proportial Hazard Test
| Endpoint | PH.test.p.value |
|---|---|
| OS | 0.0581 |
| PFS | 0.0119 |
Future Work
- Compare independent outcome assessment with composite outcome approach
- Assess the potential of hierarchical endpoint analysis such as the Win-Ratio
- Explore multi-state methods and multi-task learning approaches for multi-endpoint risk prediction
- Apply methods to more complex dataset
GitHub
The code and datasets for this project can be viewed at our GitHub repository here: https://github.com/darshu-d/MSc-Research-project-
References
- Neuhäuser, M. (2006). How to deal with multiple endpoints in clinical trials. Fundamental & clinical pharmacology, 20(6), 515-523.
- Pocock, S. J., et al. (2012). The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities. European heart journal, 33(2), 176-182.
- Jackson, C. (2011). Multi-state models for panel data: the msm package for R. Journal of statistical software, 38, 1-28.
- Wang, L., et al. (2017, November). Multi-task survival analysis. In 2017 IEEE international conference on data mining (ICDM) (pp. 485-494). IEEE.
- Ascierto, P. A., et al. (2024). Sequential immunotherapy and targeted therapy for metastatic BRAF V600 mutated melanoma: 4-year survival and biomarkers evaluation from the phase II SECOMBIT trial. Nature Communications, 15(1), 146.
- Ascierto, P. A., et al. (2023). Sequencing of ipilimumab plus nivolumab and encorafenib plus binimetinib for untreated BRAF-mutated metastatic melanoma (SECOMBIT): a randomized, three-arm, open-label phase II trial. Journal of Clinical Oncology, 41(2), 212-221.